Multiple Linear Regression - Estimated Regression Equation

Cons [t] = + 132.451174047719 + 1.13575109398718 Inc -1.51244497290197 Price + 6.27211427506502 Q1 + 5.17632146167801 Q2 + 4.69523184067709 Q3 -0.406413858128117 t + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STAT H0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	132.451174047719	35.7941985241749	3.70035311611359	0.00410610472429573	0.00205305236214787
Inc	1.13575109398718	0.281628932731224	4.03279266434991	0.00238875306692756	0.00119437653346378
Price	-1.51244497290197	0.268677292906985	-5.62922514417907	0.000218646653780161	0.000109323326890080
Q1	6.27211427506502	3.86197627164503	1.62406856849832	0.135424208443582	0.0677121042217909
Q2	5.17632146167801	4.166864023138	1.24225831055072	0.242484248158198	0.121242124079099
Q3	4.69523184067709	4.05482165482439	1.15793794163320	0.273796872327576	0.136898436163788
t	-0.406413858128117	0.892862206447003	-0.45518093967195	0.65870171011355	0.329350855056775

Multiple Linear Regression - Regression Statistics
Multiple R	0.981494957001541
R-squared	0.963332350619458
Adjusted R-squared	0.941331760991132
F-TEST (value)	43.7866605801867
F-TEST (DF numerator)	6
F-TEST (DF denominator)	10
p-value	1.30825405753043e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.71078995353254
Sum Squared Residuals	326.131218933682